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Understanding the complete light-spin interactions in magnetic systems is the key to manipulating the magnetization using optical means at ultrafast timescales. The selective addressing of spins by terahertz (THz) electromagnetic fields via Zeeman torque is, by far, one of the most successful ultrafast means of controlling magnetic excitations. Here we show that this traditional Zeeman torque on the spins is not sufficient, rather an additional relativistic field-derivative torque is essential to realize the observed magnetization dynamics. We accomplish this by exploring the ultrafast nonlinear magnetization dynamics of rare-earth, Bi-doped iron garnet when excited by two co-propagating THz pulses. By non-thermal optical pump-probe technique, we, first, find the collective exchange resonance mode between rare-earth and transition metal sublattices at 0.48 THz. We further explore the magnetization dynamics via a rather direct and efficient THz time-domain spectroscopic means. We find that the observed nonlinear trace of the magnetic response cannot be mapped to the magnetization precession induced by the Zeeman torque, while the Zeeman torque supplemented by an additional field-derivative torque follows the experimental evidences. This breakthrough not only enhances our comprehension of ultra-relativistic effects but also paves the way for the development of novel technologies harnessing light-induced control over magnetic systems., Comment: 5 figures

Context: Fault localization (FL) is the key activity while debugging a program. Any improvement to this activity leads to significant improvement in total software development cost. There is an internal linkage between the program spectrum and test execution result. Conditional probability in statistics captures the probability of occurring one event in relationship to one or more other events. Objectives: The aim of this paper is to use the conception of conditional probability to design an effective fault localization technique. Methods: In the paper, we present a fault localization technique that derives the association between statement coverage information and test case execution result using condition probability statistics. This association with the failed test case result shows the fault containing the probability of that specific statement. Subsequently, we use a grouping method to refine the obtained statement ranking sequence for better fault localization. Results: We evaluated the effectiveness of proposed method over eleven open-source data sets. Our obtained results show that on average, the proposed CGFL method is 24.56% more effective than other contemporary fault localization methods such as D*, Tarantula, Ochiai, Crosstab, BPNN, RBFNN, DNN, and CNN. Conclusion: We devised an effective fault localization technique by combining the conditional probabilistic method with failed test case execution-based approach. Our experimental evaluation shows our proposed method outperforms the existing fault localization techniques.

Peoples nowadays prefer to use digital gadgets like cameras or mobile phones for capturing documents. Automatic extraction of panels/characters from the images of a comic document is challenging due to the wide variety of drawing styles adopted by writers, beneficial for readers to read them on mobile devices at any time and useful for automatic digitization. Most of the methods for localization of panel/character rely on the connected component analysis or page background mask and are applicable only for a limited comic dataset. This work proposes a panel/character localization architecture based on the features of YOLO and CNN for extraction of both panels and characters from comic book images. The method achieved remarkable results on Bengali Comic Book Image dataset (BCBId) consisting of total $4130$ images, developed by us as well as on a variety of publicly available comic datasets in other languages, i.e. eBDtheque, Manga 109 and DCM dataset., Comment: 6 pages, 3 tables and 3 figures. Accepted at GREC 2019 in conjunction with ICDAR 2019